Hubungan antara Self Efficacy dengan Quarter Life Crisis pada Mahasiswa Psikologi Universitas Medan Area
Bibliographic record
Abstract
The purpose of this research was to find correlation between Self Efficacy and Quarter Life Crisis of Psychology students at Medan Area University. This study uses the quantitative method, the subjects of this research are all 2017 classes of Psychology Students at Medan Area University. The number of sample in this research were 87 students. The sampling techniques in this study used a Purposive Sampling Technique. The data collection method was Likert Scale. Self Efficacy scale used in this research was made by Bandura (1997) and Quarter Life Crisis scale used in this research was made by Robbins and Wilner (2001). Data analysis technique used was Correlation Product Moment. The hypothesis propodes in this research were negative correlation between Self Efficacy and Quarter Life Crisis with assumption; the higher Self Efficacy was the lower Quarter Life Crisis became and vice versa. Based on the result of the analysis carried out, there indeed were negative correlation between Self Efficacy and Quarter Life Crisis. The result were proved by hypothetical mean of Self Efficacy on 55 and Quarter Life Crisis on 72.5 then empirical mean of Self Efficacy on 65.38 and Quarter Life Crisis on 59.72. Value or coefficient which the coefficient was -0.715 with p-value significance = 0.000 0.05 with a contribution weight of 51.2% .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".